A model of antibiotic resistance genes accumulation through lifetime exposure from food intake and antibiotic treatment

Antimicrobial resistant bacterial infections represent one of the most serious contemporary global healthcare crises. Acquisition and spread of resistant infections can occur through community, hospitals, food, water or endogenous bacteria. Global efforts to reduce resistance have typically focussed on antibiotic use, hygiene and sanitation and drug discovery. However, resistance in endogenous infections, e.g. many urinary tract infections, can result from life-long acquisition and persistence of resistance genes in commensal microbial flora of individual patients, which is not normally considered. Here, using individual based Monte Carlo models calibrated using antibiotic use data and human gut resistomes, we show that the long-term increase in resistance in human gut microbiomes can be substantially lowered by reducing exposure to resistance genes found food and water, alongside reduced medical antibiotic use. Reduced dietary exposure is especially important during patient antibiotic treatment because of increased selection for resistance gene retention; inappropriate use of antibiotics can be directly harmful to the patient being treated for the same reason. We conclude that a holistic approach to antimicrobial resistance that additionally incorporates food production and dietary considerations will be more effective in reducing resistant infections than a purely medical-based approach.

In addition to playing host to a diverse collection of bacteria and other 11 micro-organism, the human gut represents a reservoir of antimicrobial resistance 12 (AMR), with antimicrobial resistance gene (ARG) abundance known to be correlated at 13 a population scale with antibiotic use [7]. Enteric bacteria hold ARGs either on their 14 chromosomes or on mobile genetic elements; it is thought that these ARGs 15 predominantly reside within non-pathogenic unclassified species [8]. 16 Antimicrobial resistances in the gut may become established after ingestion of 17 contaminated food. Many studies have shown ARGs to be present in a range of 18 high-risk food products: raw and cooked meats [9][10][11][12][13], fermented milk products [14][15][16], 19 fermented meat products [17,18] and vegetable products [19][20][21]. The ready-to-eat food 20 market is particularly problematic due to a lack of cooking and washing before 21 consumption [22][23][24][25]. 22 Several authors have identified systematic differences in ARG levels between 23 individuals resident in different countries [7,26,27]. One source of these differences is 24 likely to result from differing levels of availability of antibiotic treatments. 25 Governmental approaches to antibiotic availability are diverse and defined daily doses 26 per inhabitant can vary widely [7,28,29]. 27 Ageing individuals are at higher risk of AMR infections because of increased 28 exposure to ARGs, increased lifetime exposure to antibiotics and increased vulnerability 29 to infection with age. The number of ARGs within an individual's intestinal tract is 30 correlated with age [30,31]. Lu et al. [30] showed that the number of resistances from 31 faecal samples of four different age groups were positively correlated with increasing age. 32 Further, cluster analysis suggested that resistances were being acquired and 33 accumulated over time rather than being transient. Other research has also supported 34 this view. Ghosh et al. [8] profiled resistance genes of 275 gut flora samples sourced from 35 multiple countries and found increasing ARG diversity with age. Antimicrobial resistant 36 bacterial infections in older age can often result from endogenous bacteria moving from 37 the intestinal tract to other areas of the body, for example the urinary tract [32,33].

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In this work, we bring together these aspects of ARG establishment using a 39 probabilistic mathematical model of the accumulation of antimicrobial resistance over

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We have defined a probabilistic model to define the acquisition of ARGs in the enteric 48 system of individuals (figure 1), which we have evaluated using Monte Carlo simulations. 49 We consider the acquisition of resistance genes to 14 different classes of antibiotics. In 50 order to reduce model complexity, we consider resistance to individual classes of 51 antibiotics (e.g. beta lactams, carbapenems, cepholosporins, aminoglycosides, etc), 52 rather than specific antibiotics. This is purposefully simplified view of resistance to 53 different antibiotics: there is significant variation in resistance genes of antibiotics 54 within the same class and indeed variation between ARGs conferring resistance to the 55 same antibiotic (e.g. there are over 40 different genes divided into 11 different classes of 56 action which encode resistance to tetracycline [34]).

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For each antibiotic class, we consider the probability that an individual is exposed to 58 resistance genes through ingestion of food, and the probability of these resistance genes 59 becoming fixed in the individual's enteric bacterial communities. The probability of week. The probability of an individual being exposed to resistance via food intake each 79 week (P FoodRes. ) is a random variable with a uniform distribution. Once exposed to 80 resistance, there is a probability that this resistance shall establish in the microbial flora 81 in the gut of the individual (P Fix ). Each week, there is an independent probability that 82 the individual may undergo antibiotic treatment (P Ab. Treat. ). As the use of antibiotics 83 can exert selective pressures on resistant bacterial populations, we assign a greater 84 probability of establishment of ARGs in the presence of antibiotic treatment (P Ab. Fix. ). 85 The probability that the individual acquires new class of resistance in any given week is 86 given by the transition probability (1).
The parameters used for each scenario are given in the table 1. At each time step in 88 the model, we sample the probability of resistance in food intake from a continuous data for human gut microbiota in areas of different levels of antibiotic use [30]. 94 We conducted a local sensitivity analysis for each of the model parameters. For each 95 parameter, we take 1000 parameter values sampled from the feasible parameter space 96 Probability of resistance genes for antibiotic class A i being present in an individual's food intake Probability of resistance genes becoming established in an individuals resistome in absence of antibiotics Probability of resistance genes for antibiotic class A i becoming established in an individuals resistome in presence of antibiotics Probability of resistance genes for antibiotic class A i being lost acquired resistance genes to be lost. In order to simulate the possible loss of ARGs from 99 the resistome through wash out, at the end of each time step in the Markov chain model 100 there is a possibility that an acquired resistance, A i , is lost with probability P Loss (A i ). 101 MATLAB R2020b was used to run time course simulations of the lifetime resistance 102 model and to perform a local sensitivity parameter analysis of the model.

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Simulation of 1000 individuals in each of the 3 antibiotic usage scenaios (low, medium 105 and high) for the standard parameter set (table 1) shows that higher antibiotic use 106 increases ARG acquisition over time by commensal bacteria (figure 2).  We vary the model parameters (P Ab. Treat. , P Fix , P Ab. Fix , and β food ) across the possible parameter space (given in table 1) and then calculated the mean ARG load at age 70 of 1000 individuals for each of the different parameter values. For P Ab. Treat. , the black line shows the mean resistance load as the probability of undergoing antibiotic treatment is varied across the parameter space, and the dashed red, blue and green lines indicate the parameter values used for P Ab. Treat. in the low, medium and high antibiotic use areas respectively. For the local sensitivity analyses of P Fix , P Ab. Fix , and β food res. , the red, blue and green lines represent the average resistance load as the parameter of interest is varied for low, medium and high antibiotic use areas respectively. The dashed black line in these subplots represents the values used for these parameters in the model simulations. Another avenue of control is the reduction of ARG intake through food and water. 129 We considered two possibilities: an overall reduction of ARGs in food (and water) at all 130 times; and a reduction only during antibiotic treatment, representing dietary change 131 during such treatment, e.g. avoiding higher risk or raw foods. We considered two levels 132 of reduction, 20% and 50% for both antibiotic usage and ARG levels in food. All 133 scenarios were applied for low, medium and high antibiotic use countries. Figure 4 134 shows the effect of reducing the probability of undergoing antibiotic treatment alone 135 and figure 5 the effect of reducing the probability of ARG in food alone. As expected, 136 we observe that both these approaches result in a reduction in ARG acquisition over a 137 lifetime, and reducing either ARG intake or antibiotic consumption by 50% gives a 138 greater effect than reducing by 20%.

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Reducing the antibiotic consumption in areas that have a higher rate of antibiotic 140 treatment is on average more effective at reducing the ARG load: a 20% reduction in 141 Ab usage yields an average 5.98% and 11.33% reduction in resistance load by age 70 in 142 high and medium use areas respectively, while a 50% reduces the mean resistance load 143 by 21.21% and 32.92%. Comparatively a reduction in ARG intake via food is more 144 effective at reducing ARG acquisition in low antibiotic usage areas with a 20% and 50% 145 reduction in ARG intake giving a 13.11% and 35.23% reduction in the mean resistance 146  Reducing ARG intake via food during periods of antibiotic 148 treatment is particularly effective at limiting acquisition of 149 resistance genes.

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The greatest reduction in the number of resistance classes acquired by 70 years comes 151 for a combined approach. Here for even a modest 20% decrease in both actions we see a 152 reduction of between 12.63% and 24.02%, depending upon the original level antibiotic 153 usage. For a 50% decrease in both, we observe the number of resistance classes acquired 154 by 70 years can be reduced significantly (by between 46.35% and 56.52%) reducing the 155 likelihood that endogenous bacterial infections in older age will be resistant to 156 treatment. For the case of reduction only at times of antibiotic use, we saw a similar 157 reduction in ARG load as for the reduced probability of ARG in food in general (Figure 158 6, S1 Fig & S2 Fig). Inclusion of ARG loss from resistome does not significantly 160 impact lifetime resistance model simulations for realistic values 161 of P Loss . 162 We adapted the lifetime model to include the possibility of resistance loss due to ARG 163 washout or other factors. At the end of each time step in the markov chain model, there 164 is a possibility that an acquired resistance, A i , may be lost with probability P loss (A i ). 165 We then simulated the lifetime model with ARG loss (figure 7(a)) for 1000 individuals 166 in each of the three antibiotic usage areas using the parameter values given in table 1. 167 A comparison of the results of the simulation with ARG loss, figure 7(a), and without 168 ARG loss, figure 2(b), shows negligible differences between the models for each of the 169 three antibiotic usage scenarios considered. 170 We then performed a local sensitivity analysis of the lifetime model with ARG loss 171 to the parameter P loss ( figure 7(b)). Sensitivity analysis showed that the average 172 resistance load was consistent with the standard lifetime model without ARG loss when 173 the probability of resistance loss was less than 10 −4 , while for P loss greater than 10 −4 , 174 we see that the chance of ARG loss is high enough that it leads to a significant 175 reduction on the average resistance load. However, it is important to note that this  We have shown that the long-term acquisition and retainment of genes providing 181 resistance to different classes of antibiotics can be reduced by at three implementable 182 factors. First, the number of resistance genes acquired by and individual is dependent 183 upon the use of antibiotics over an individual's lifetime. A conservative approach to 184 antibiotic availability and dosing guidelines, as already implemented in many countries, 185 and as advocated in much of literature on antibiotic resistance, would be a practical 186 approach to reducing the long-term number of acquired resistances. Indeed, the 187 converse is true, in that unnecessary antibiotic treatment can lead to long-term harm to 188 the patient being treated, and could be considered unethical; this argument stands in 189 contrast to the more standard argument that the risk over-use is primarily to patients 190 other than the one being treated. Second, the number of acquired genes can be reduced 191 even further if an individual's intake of resistance genes, carried on both pathogenic and 192 non-pathogenic bacteria, is also reduced. This could be achieved by policy and practice 193 changes in the food supply chain, including agriculture and post-harvest food 194 production. Third, the reduction in intake of resistance genes is particularly effective 195 during periods of antibiotic treatment where selective pressures increase the likelihood 196 of the retainment of genes. We would suggest that dietary advice should be given to 197 those undergoing antibiotic treatment to avoid products at higher risk of carrying ARGs 198 (even on non-pathogens), as well as ensuring that all food consumed during treatment is 199 fully cooked. The level of benefit to be gained from alterations in medical treatment 200 and dietary changes is highly dependent upon the level of antibiotic use, which varies 201 greatly between countries. Whilst our general model shows benefit at all prescribing 202 levels, a differentiated model looking at region-and country-specific practices, as well as 203 containing specific details of antibiotic classes and associated resistance genes, would be 204 better able to be quantify the potential benefits of such changes.